Stanley E Young PE PhD University of Maryland Center for Advanced Transportation Technology Traffax Inc Outline Vehicle Probe Project Where we have been where we are now Completing the Picture ID: 749771
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Slide1
Completing the Real-Time Traffic Picture
Stanley E. Young, P.E. Ph.D.
University of Maryland
Center for Advanced Transportation Technology
Traffax Inc.Slide2
Outline
Vehicle Probe Project
Where we have been, where we are now ….
Completing the Picture
Bringing in Volume Data – State Wide
Extending Real-Time to Arterial Networks
Its time for Arterial Management Systems …Slide3
I-95 Corridor Coalition
Maine to Florida
Road, Rail, and Water Transport
DOTs, Toll authorities, Public Safety, and other related organizations
Forum for management and operations issues
Volunteer organization
Goal: Improve Transportation System PerformanceMulti-jurisdictional cooperation, >20 yearsSlide4
Vehicle Probe Project
Phase I (2008-2014)
First Probe-based Traffic System
Specifications-based, validated
Licensing - one buys, all share
Began 2.5K miles, grew to 40K
Travel time on signs, 511 systems, operational awareness, performance measuresPhase II (2014 forward)All of the aboveBetter quality, less costData market place (Multiple-vendors)Emphasis on arterials and latency42.5K and growingMap-21 Performance MeasuresSlide5
Vehicle Probe Project
Phase I (2008-2014)
First Probe-based Traffic System
Specifications-based, validated
Licensing - one buys, all share
Began 2.5K miles, grew to 40K
Travel time on signs, 511 systems, operational awareness, performance measuresPhase II (2014 forward)All of the aboveBetter quality, less costData market place (Multiple-vendors)Emphasis on arterials and latency42.5K and growingMap-21 Performance MeasuresSlide6
First Multi-Vendor Freeway Validation
I-83
& I-81
Harrisburg, Oct 2014
PA-08
14 Segments
31.3 milesData collection2300 to 2555 total hrs71 to 80 hrs [0-30]53 to 66 hrs [30-45]AASE2.1 to 4.1 mph [0-30]3.1 to 5.8 mph [30-45]
6
May 07, 2015Slide7
PM Peak Hour (Oct 15-16, 2014)
May 07, 2015
7Slide8
May 07, 2015
8
Non-recurring Congestion
Oct 13, 2014 10 AM to 7 PMSlide9
Integrating Real-Time Volume
Objective: Provide volume data in real-time on freeways and high-level
a
rterials in a method similar to probe based speed and travel time data
Approach
Cooperative Research Initiative with IndustryCalibration/validation
test bedFocus group to refine productVendors develop, test, and reportGoal is to accelerate timeframe to viable real-time volume data feedIf interested contact rmjcar@umd.edu Slide10
Arterial Probe Data Quality Study
2013 – mid 2014
10
State / Set ID
Road Number
Road Name
Validation Date Span
# of Segments
# of Through Lanes
AADT Range (in 1000s)
Length*
(mile)
# Signals / Density
# of Access Points
Median Barrier
Speed Limit
(mph)
NJ-11
US-1
Trenton Fwy, Brunswick Pike
Sep 10 - 24, 2013
10
2-4
33 - 90
14.2
10 / 0.7
112
Yes
55
NJ-42
Black Horse Pike
8
2
25-5412.523 / 1.8260Yes45-50US-130Burlington Pike1034214.328 / 2.0229Yes50NJ-12NJ-38Kaighn Ave.Nov 5-19, 2013162-432-8024.544 / 1.8235Yes50NJ-73Palmyra Bridge Rd.182-433-7423.941 / 1.7236Yes45-55PA-05US-1Lincoln HighwayDec 3 - 14, 2013282 - 3+321 - 10030.62107 / 3.5178Yes40 - 50US-322Conchester Highway61-222 - 3414.287 / 0.548No35 - 45PA-06PA-611Easton RdJan 9 - 22, 2014102-418-316.721/ 3.1398NO40-45PA-611Old York Rd81-221-307.326/ 3.56105Partial15-40PA-611N Broad St162-417-329.6102/ 10.62161NO15-40VA-07VA-7Leesburg Pike and Harry Byrd HwyApril 5-16, 2014302-445-6030.557 / 1.9203Yes35-55US-29Lee Hwy (S Washington St) 4214-254.422 / 5114Partial30VA-08US-29Lee HwyMay 8-19, 2014262-415-4531.9115/3.6287Partial35-50MD-08MD-140Reistertown RdJune 5-14, 2014121 - 319-4410.540 / 3.8148No30-40Baltimore Blvd62 - 440-5311.016/ 1.538YES45-55
9 Case Studies from 2013-14Spans NJ through NCTest extent of probe data15K AADT to 100K2 – 12 lanes0.5 to 10+ signals per mileObjective: Reference case studies
April 30, 2015Slide11
Arterial Probe Data
Recommendations
11
Probe data quality most correlated to signal density
Increased volume aids probe data quality, but does not overcome issues resulting from high signal density
Accuracy anticipated
to improve with increased probe density and better processing
April 30, 2015
Likely
to have usable probe data
Possibly
usable probe data
Likely not usable probe
data
<= 1 signals per mile
AADT > 40000
Fully or Partially captures
>75% slowdowns
<= 2 signals per mile
AADT 20K to 40K
May Fail to capture > 25% of slowdowns
Should be tested
>=2 signals per mile
Not recommendedSlide12
Additional Insights
12
Not ready for Prime Time
Probe data
usable
on highest class arterials
Signal density < 1 per mile on averageTravel times are proportional to ground truthMay still miss some slowdowns, and may want to testConsistent positive bias at low speedsAs probe data improves, delay will increase
Remaining Challenges
Severe
queuing / multi-cycle delays
Optimistic bias with bi-modal traffic
Not sensitive to
signal timing changes
Major disruptions
problematic
April 30, 2015Slide13
Roadmap for Arterial Management SystemsSlide14
Technologies Enabling Arterial Management Systems
Re-identification
Samples vehicle travel time (5% for BT)
Works best at corridor level
Independent of Signal
System
Provides top-level user experience information
High-Res Signal Data
Logs
all
actuation and phasing information
Works best at intersection level
Integrated with Signal System
Provides detailed intersection analysis
Both enabled by consumer wireless communication and big data processing.
Available Now – Multiple Vendors - Cost Effective
Not one or the other… but both!Slide15
Emerging Arterial Performance Measures
Travel Time and Travel Time Reliability – based on sampled travel time sources
Initially re-identification data, later outsourced probe data as it matures, as well as connected vehicle data
Percent Arrivals on Green
Supported by methods such as Purdue Coordination Diagram tools
Split Failures (occurrences)Related to GOR / ROR and similar analysisSlide16
Re-Identification Data (Bluetooth)
Uses a ID unique to a vehicle (MAC ID of a Bluetooth device inside vehicle)
An initial detector identifies when a vehicle enters a corridor by the vehicle’s ID
Another detector
re-identifies
the vehicle at the end of the corridor
Travel time/ speed can be directly calculated from the entry and exit time16
Picture source: libelium.com
Direct samples of Travel TimeSlide17
Re-identification Data FidelitySlide18
Statistical Performance MeasuresSlide19
High Resolution Signal Data
Logging of sensor and phase information
Data forwarded periodically to central server
Applications
Purdue Coordination Diagram
Red-Occupancy Ration / Green Occupancy RatioVolume / Demand Analysis (per movement)
Streamlined MaintenancePicture Source: FHWASlide20
Sample Metric - Intersection
Purdue
Coordination DiagramSlide21
Sample Metric - Intersection
Movement Capacity Analysis (ROR – GOR)Slide22
Current State of Arterial Management Systems (AMS)Slide23
Benefits
Created a language between traffic
engineers, planners, operations,
and management to establish goals, measure performance, and manage the system
Link performance to budget/funding
Systematic approachLong term performance tracking
Better utilization of professional staffOrganizational maturitySlide24
Real-Time Arterial Performance
Stuff from Chapter 1Slide25
Travel-Time Visualization
Picture of Cascading CFD
Picture of ROR and GORSlide26
Intersection Impact – ROR & GORSlide27
Thank You!
Stanley E. Young, P.E. Ph.D.
University of Maryland
Center for Advanced Transportation Technology
5000 College Ave, Bldg 806, #2203
College Park, 20742Mobile 301-792-8180
seyoung@umd.edu